| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 3 | | adverbTags | | 0 | "he said obliquely [obliquely]" | | 1 | "he said flatly [flatly]" | | 2 | "she said weakly [weakly]" |
| | dialogueSentences | 12 | | tagDensity | 0.917 | | leniency | 1 | | rawRatio | 0.273 | | effectiveRatio | 0.273 | |
| 80.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 759 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "suddenly" | | 2 | "sharply" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 40.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 759 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "silence" | | 1 | "glint" | | 2 | "eyebrow" | | 3 | "sense of" | | 4 | "familiar" | | 5 | "treacherous" | | 6 | "fluttered" | | 7 | "pounding" | | 8 | "cataloged" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 44 | | matches | | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 44 | | filterMatches | | | hedgeMatches | | 0 | "managed to" | | 1 | "tried to" | | 2 | "seemed to" | | 3 | "tended to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 756 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 69.71% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 685 | | uniqueNames | 9 | | maxNameDensity | 1.61 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 11 | | Economist | 1 | | Dalston | 1 | | Lucien | 8 | | Malphora | 1 | | Eva | 1 | | Silas | 1 | | Don | 1 | | Yu-Fei | 1 |
| | persons | | 0 | "Rory" | | 1 | "Lucien" | | 2 | "Eva" | | 3 | "Silas" | | 4 | "Yu-Fei" |
| | places | | | globalScore | 0.697 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 756 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 42 | | std | 21.77 | | cv | 0.518 | | sampleLengths | | 0 | 46 | | 1 | 58 | | 2 | 65 | | 3 | 47 | | 4 | 53 | | 5 | 24 | | 6 | 6 | | 7 | 61 | | 8 | 18 | | 9 | 26 | | 10 | 45 | | 11 | 57 | | 12 | 32 | | 13 | 26 | | 14 | 10 | | 15 | 98 | | 16 | 45 | | 17 | 39 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 44 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 120 | | matches | (empty) | |
| 79.37% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 45 | | ratio | 0.022 | | matches | | 0 | "Nearly midnight — who could be calling at this hour?" |
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| 91.34% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 690 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 24 | | adverbRatio | 0.034782608695652174 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.02318840579710145 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 16.8 | | std | 7.79 | | cv | 0.464 | | sampleLengths | | 0 | 11 | | 1 | 25 | | 2 | 10 | | 3 | 4 | | 4 | 19 | | 5 | 14 | | 6 | 21 | | 7 | 6 | | 8 | 6 | | 9 | 28 | | 10 | 25 | | 11 | 23 | | 12 | 24 | | 13 | 19 | | 14 | 14 | | 15 | 20 | | 16 | 24 | | 17 | 6 | | 18 | 12 | | 19 | 8 | | 20 | 25 | | 21 | 16 | | 22 | 18 | | 23 | 26 | | 24 | 6 | | 25 | 27 | | 26 | 12 | | 27 | 6 | | 28 | 21 | | 29 | 10 | | 30 | 2 | | 31 | 18 | | 32 | 32 | | 33 | 15 | | 34 | 11 | | 35 | 10 | | 36 | 32 | | 37 | 26 | | 38 | 20 | | 39 | 20 | | 40 | 17 | | 41 | 17 | | 42 | 11 | | 43 | 13 | | 44 | 26 |
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| 97.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.7555555555555555 | | totalSentences | 45 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 43 | | matches | | 0 | "Nearly midnight — who could" | | 1 | "Finally, unwilling to risk the" | | 2 | "Once inside, he would realize" |
| | ratio | 0.07 | |
| 89.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 43 | | matches | | 0 | "She fumbled with the unfamiliar" | | 1 | "Her breath caught in her" | | 2 | "She couldn't let Lucien know" | | 3 | "he said obliquely, shoulders tensing" | | 4 | "She tried to shut the" | | 5 | "he said flatly" | | 6 | "Her treacherous heart fluttered at" | | 7 | "she growled, endeavoring to put" | | 8 | "His tone was bland, unconcerned," | | 9 | "She hesitated for a long" | | 10 | "He cut her off sharply" | | 11 | "His true nature shone clear" | | 12 | "Her every instinct screamed at" | | 13 | "she said weakly, hands trembling" |
| | ratio | 0.326 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 29 | | totalSentences | 43 | | matches | | 0 | "A hesitant knock at the" | | 1 | "Silence greeted her inquiry." | | 2 | "Rory sighed, cinching the sash" | | 3 | "She fumbled with the unfamiliar" | | 4 | "The door swung open, revealing" | | 5 | "Her breath caught in her" | | 6 | "Rory's voice shook with anger" | | 7 | "She couldn't let Lucien know" | | 8 | "Lucien's heterochromatic gaze swept over" | | 9 | "he said obliquely, shoulders tensing" | | 10 | "She tried to shut the" | | 11 | "he said flatly" | | 12 | "Her treacherous heart fluttered at" | | 13 | "she growled, endeavoring to put" | | 14 | "His tone was bland, unconcerned," | | 15 | "She hesitated for a long" | | 16 | "The door clicked shut behind" | | 17 | "Rory fiddled with the three" | | 18 | "An admission, however unwilling, of" | | 19 | "Those names are no more," |
| | ratio | 0.674 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 43 | | matches | | | ratio | 0.023 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "Each word ricocheted through the air, its nuances bouncing off walls, the dusty smell of the stacks of books and scrolls overwhelming nearly every other sensati…" | | 1 | "Rory shook her head, trying to clear her thoughts as Lucien watched her implacably, waiting for an opening to exploit." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "she said weakly, hands trembling as she gestured to the chaise longue" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "she growled (growl)" | | 1 | "Lucien drawled (drawl)" |
| | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.4 | | effectiveRatio | 0.333 | |